Agents are computational entities that can be viewed as
perceiving their environment through sensors and acting on the environment
through affectors. Multi-agent systems are collections interacting, intelligent
autonomous agents that are in pursuit of some set of goals or need to perform
some set of tasks. Distributed AI relates to the construction and application of
multi-agent systems.

Multi-agent systems have become of increased interest in the
past decade for the following reasons. The first reason is that multi-agent
systems can be a useful modeling tool in the design of modern computing
platforms which are distributed, large, and heterogeneous in nature. A second
reason is that multi-agent systems can be useful in understanding and modeling
theories of the evolution of complex social systems. A brief description of a
current laboratory project funded by the National Science Foundation Intelligent
Systems division, IIS-9907257 that relates to the latter reason is now given.

PROJECT SUMMARY

A state is among the most sophisticated and powerful
structures that has emerged from the social evolution process. In the modern
world these are termed "nation statesí with a government composed of a
hierarchical decision-making structure where the decision-makers are either
elected or appointed. States are supported by various economies and are able to
interact with each other via warfare, trade, etc. Most states in the ancient
world-often called archaic states-were ruled by hereditary royal families. These
archaic states exhibited much internal diversity with populations numbering from
tens of thousands to millions. They had a bureaucracy, organized religion, a
military presence, large urban centers, public buildings, public works, and
services provided by various professional specialists. The state itself could
enter into warfare and trade-based relationships with other states and less
complex neighbors.

The process by which complex social entities such as the
state emerged from lower level structures and other supporting economies has
long been of prime interest to anthropologists and other disciplines as well.
This is because the emergence of such a social structure can have a profound
impact on the societiesí physical and social environment. However, the task of
developing realistic computational models that aid in the understanding and
explanation of state emergence has been a difficult one. This is the result of
two basic factors:

The process of state formation inherently takes place on a
variety of temporal and spatial scales. The emergence of hierarchical
decision-making can be viewed as an adaptation that allows decision-makers to
specialize their decisions to particular spatial and temporal scales.

The formation of the state is a complex process that is
fundamentally directed by the social variables but requiring dynamic
interaction between the emergent system and its environment. Identifying the
nature of these interactions is one of the reasons why the process of state
formation is of such interest.

The goal of this project is to produce a large-scale
knowledge-based computational model of the origins of the Zapotec State,
centered at Monte Alban, in the Valley of Oaxaca, Mexico. State formation took
place between 1400 BC. and 300 BC. While archaic states have emerged in various
parts of the world, the relative isolation of the valley allowed the processes
of social evolution to be more visible there. Extensive surveys of the valley
were undertaken by the Oaxaca Settlement Pattern Project in the 1970ís and
1980ís. The location and features of nearly 3,000 sites dating from the archaic
period (8000 BC.) to Late Monte Alban V (just prior to the arrival of the
Spaniards) were documented. Several hundred variables were recorded for each
site. These data are the basis for generating the knowledge used in our model.
Over the past 5 years we have developed a database to house this data. We have
used techniques from machine learning (genetic and cultural algorithms) and data
mining to extract information about site settlement decision-making, as well as
other facets of the social evolution process. The project has three phases:

Year 1: We will complete the analysis of the Oaxaca
Settlement Survey data in order to produce knowledge about warfare, trade, and
economic decisions. The construction of the basic computational model of state
formation will begin. The approach will center on the use of paradigms from
evolutionary learning, such as Genetic and Cultural Algorithms.

Year 2: In this phase the development of a prototype
model of state evolution will take place. Since the Valley of Oaxaca consists of
5 different sub-regions, each interacting with the emerging state in different
ways at different times, a distributed model is proposed here. The complexity of
the model will require that each regional model runs on a separate network site,
and interact with each other at different temporal and spatial scales through
the network.

Year 3: The model will then be used to test a various
hypotheses concerning the emergence of complex systems. These hypotheses relate
to the importance of processes such as chiefly cycling, hydraulic despotism,
social circumscription, consolidation of resources, and territorial expansion
among others. The results of the simulations will be compared with the patterns
observed in the actual site settlement data for the valley.

The laboratory has also been a focus for the development of
evolution-based machine learning tools for use in modeling the cultural
evolution in multi-agent systems and for data-mining of the large-scale data
sets associated with such systems. Dr. Reynolds has developed Cultural
Algorithms, a framework for modeling the evolution of social systems. Cultural
Algorithms have been used to solve a variety of real world problems, including
function optimization, knowledge base design and re-engineering, software
design, in addition to the application above. Over 100 articles and two books
have been produced by laboratory research in the past.